Mutual information and gamma test for input selection

ESANN 2005 Proceedings - 13th European Symposium on Artificial Neural Networks

Volumn , Issue , 2007, Pages 503-508

  Reyhani, Nima ^a   Hao, Jin ^a   Ji, Yongnan ^a   Lendasse, Amaury ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTION APPROXIMATION; GAMMA TEST; INPUT SELECTION; MUTUAL INFORMATIONS; OVERFITTING; TIME SERIES PREDICTION;

NEURAL NETWORKS;

MEAN SQUARE ERROR;

EID: 84886993129     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. R. de Figueiredo, Implications and applications of Kolmogorov's superposition theorem, IEEE Tran. on Automatic Control, Vol. 25, Issue 6, p.p. 1227-1231, Dec 1980.
2. N. Kwak, CH. Choi; Input feature selection for classification problems, Neural Networks, IEEE Transactions on Volume: 13, Issue: 1, pp. 143-159, Jan 2002.
3. D. Zongker, A. Jain, Algorithms for feature selection: An evaluation Pattern Recognition, Proceedings of the 13th International Conference on Volume: 2, 25-29 pp. 18-22 vol.2, Aug 1996.
4. X Chen. An improved branch and bound algorithm for feature selection. Pattern Recognition Letters. Volume 24, Issue 12 (August 2003) Pages: 1925-1933. ISSN: 0167-8655. 2003.
5. E. P. Xing, M. I. Jordan, R. M. Karp. Feature Selection for High-Dimensional Genomic Microarray Data. Proc. of the Eighteenth International Conference in Machine Learning, ICML2001.
6. V. Fabio, J. W. Christian. Variational Bayesian Feature Selection for Gaussian Mixture Models. Feature Analysis for ASR, TTS, and Verification. SP-P6.3.
7. B. Efron, R. J. Tibshirani, An introduction to the bootstrap, Chapman & Hall, 1993.
8. B. Efron, R. Tibshirani. Improvements on cross-validation: The.632+ bootstrap method. J. Amer. Statist. Assoc. 92:548-560, 1997.
9. M. Stone, An asymptotic equivalence of choice of model by cross-validation and Akaike's criterion, J. Royal. Statist. Soc., B39, 44-7, 1977.
10. R. Kohavi. A study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, Proc. of the 14th Int. Joint Conf. on A.I., Vol. 2, Canada, 1995.
11. B. Efron, Estimating the error rate of a prediction rule: improvements on cross-validation. Journal of American Statistical Association, 78(382):316-331, 1983.
12. A. J. Jones, New Tools in Non-linear Modeling and Prediction. Computational Management Science, Vol. 1, Issue 2, p.p. 109-149, 2004.
13. D. Evans and A. J. Jones, A proof of the Gamma test, Proc. Roy. Soc. Lond. A, Vol. 458, pp. 1-41, 2002.
14. H. H. Yang and S. Amari, Adaptive online learning algorithms for blind separation: Maximum entropy and minimum mutual information Neural Comput, vol. 9, pp. 1457-1482, 1997.
15. A. M. Fraser and H. L. Swinney, Phys. Rev. A 33, 1134 (1986).
16. A. Kraskov, H. Stögbauer, P. Grassberger, Estimating mutual information Phys. Rev. E, in press
17. J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002 (ISBN 981- 238-151-1).
18. J. A. K. Suykens, J. De brabanter, L. Lukas, J. Vandewalle, Weighted least squares support vector machines: robustness and sparse approximation, Neurocomputing, Special Issue on fundamental and information processing aspects of neurocomputing, 48 (1-4), pp. 85-105, 2002.
19. M. Cottrell, B. Girard, P. Rousset, Forecasting of curves using a Kohonen classification. Forecasting 17, pp. 429-439, 1998.
20. N. Kwak, C. Choi, Input Feature Selection by Mutual Information Based on Parzen Window,IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, Issue 12, pp. 1667-1671, Dec 2002.